这与其他问题类似，但不是重复问题。但是，我仍然无法得到正确的结果。
我基本上尝试记录两个Wav文件（1 - 基本文件2 -Temp文件），然后将其转换为字节并传递给Aforge FFT，然后传递相关性。
很少有困惑。当我录制文件时，我正在使用16位的44100 Khz。因此我相信它将返回每秒44100字节。 FFT接受2的幂的字节，所以我一次传递16384个字节并将其存储到父数组，然后我使用Cross Corelation Alogorithm来查看相似性，它只返回0.30一直。我再次不确定我是否采取了正确的方式。
我附上了示例代码和相关参考资料。
static void Start()
{
waveSource = new WaveInEvent();
//waveSource.WaveFormat = new WaveFormat(44100, 1);//44khz rate
waveSource.WaveFormat = new WaveFormat(44100, 16, 1);
waveSource.DataAvailable += new EventHandler<WaveInEventArgs>(waveSource_DataAvailable);
waveSource.RecordingStopped += new EventHandler<StoppedEventArgs>(waveSource_RecordingStopped);
Random rnd = new Random();
int card = rnd.Next(52);
waveFile = new WaveFileWriter(@Environment.CurrentDirectory.ToString() + @"\Recording" + card.ToString() + "0001.wav", waveSource.WaveFormat);
waveSource.StartRecording();
}
private static void FileCompare(string file1, string file2)
{
double[] r1;
// readWav(file1, out permanent, out r);
//readWav(file2, out temp, out l);
openWav(file1, out permanent, out r1);
openWav(file2, out temp, out r1);
double[] odoubledata = new double[163840];
double[] odoubledata1 = new double[163840];
int n = 0;
int k = 0;
for (int lk = 0; lk <10; lk++)
{
// if (lk != 0 || lk != 9)
{
AForge.Math.Complex[] c = new AForge.Math.Complex[16384];
for (int i = 0; i < 16384; i++)
{
c[i].Re = permanent[i];
c[i].Im = 0;
}
AForge.Math.Complex[] c1 = new AForge.Math.Complex[16384];
for (int i = 0; i < 16384; i++)
{
c1[i].Re = temp[i];
c1[i].Im = 0;
}
FourierTransform.FFT(c, FourierTransform.Direction.Forward);
FourierTransform.FFT(c1, FourierTransform.Direction.Forward);
// FourierTransform.DFT(c1, FourierTransform.Direction.Forward);
double[] doubledata = new double[c.Length];
double[] doubledata1 = new double[c1.Length];
for (int i = 0; i < c.Length; i++)
{
doubledata[i] = c[i].Re;
odoubledata[k] = c[i].Re;
k = k + 1;
}
for (int i = 0; i < c1.Length; i++)
{
doubledata1[i] = c1[i].Re ;
odoubledata1[n] = c1[i].Re;
n = n + 1;
}
}
}
double temq2;
int off;
CalcCrossCorrelation(odoubledata, odoubledata1, out off, out temq2);
Console.WriteLine("Similarity " + temq2);
}
参考资料 - 存储Wav文件https://stackoverflow.com/a/17983876/4124478
阅读Wav文件https://stackoverflow.com/a/11162668/4124478
互相关https://stackoverflow.com/a/27277120/4124478
FFT代码https://stackoverflow.com/a/170413/4124478
更新
我正在存储具有相同字节的示例文件，因为它很容易比较。录音将在4秒后自动停止。
static void Start()
{
waveSource = new WaveInEvent();
//waveSource.WaveFormat = new WaveFormat(44100, 1);//44khz rate
waveSource.WaveFormat = new WaveFormat(8192, 16, 1);
waveSource.DataAvailable += new EventHandler<WaveInEventArgs>(waveSource_DataAvailable);
waveSource.RecordingStopped += new EventHandler<StoppedEventArgs>(waveSource_RecordingStopped);
Random rnd = new Random();
int card = rnd.Next(52);
waveFile = new WaveFileWriter(@Environment.CurrentDirectory.ToString() + @"\Recording" + card.ToString() + "0001.wav", waveSource.WaveFormat);
waveSource.StartRecording();
}
static void waveSource_DataAvailable(object sender, WaveInEventArgs e)
{
if (waveFile != null)
{
waveFile.Write(e.Buffer, 0, e.BytesRecorded);
int seconds = (int)(waveFile.Length / waveFile.WaveFormat.AverageBytesPerSecond);
if (seconds > 4)
{
waveFile.Flush();
Stop();
}
}
}
另外，我无法发送所有字节，因为总长度不是2的幂。因此，我一次只发送2个字节，通常我得到0.60相似度。
private static void FileCompare(string file1, string file2)
{
double[] l;
double[] r;
double[] r1;
// readWav(file1, out permanent, out r);
// readWav(file2, out temp, out l);
openWav(file1, out permanent, out r1);
openWav(file2, out temp, out r1);
double[] odoubledata = new double[41769];
double[] odoubledata1 = new double[41769];
Console.WriteLine("-------cross correlation--------");
int n = 0;
int k = 0;
for (int i = 0; i < permanent.Length; i = i + 2)
{
Complex[] test1 = new Complex[2];
test1[0].Re = permanent[n];
test1[0].Im = 0;
test1[1].Re = permanent[n + 1];
test1[1].Im = 0;
FourierTransform.FFT(test1, FourierTransform.Direction.Forward);
odoubledata[n] = test1[0].Magnitude + test1[0].SquaredMagnitude;
odoubledata[n + 1] = test1[1].Magnitude + test1[1].SquaredMagnitude;
n = n + 1;
}
for (int i = 0; i < temp.Length; i = i + 2)
{
Complex[] test1 = new Complex[2];
test1[0].Re = temp[k];
test1[0].Im = 0;
test1[1].Re = temp[k + 1];
test1[1].Im = 0;
FourierTransform.FFT(test1, FourierTransform.Direction.Forward);
odoubledata1[k] = test1[0].Magnitude + test1[0].SquaredMagnitude;
odoubledata1[k + 1] = test1[1].Magnitude + test1[1].SquaredMagnitude;
k = k + 1;
}
double temwe2;
int offs;
CalcCrossCorrelation(odoubledata, odoubledata1, out offs, out temwe2);
Console.WriteLine("Similarity Total together " + temwe2);
}
我不确定我是否通过将“幅度”和“平方幅度”相加来存储正确的输出值。